Field of the Invention
The present invention relates to a technique for detecting anomalies in vehicles, industrial machinery, and the like.
Description of the Related Art
Because an accident occurring in industrial machinery at a railway or plant has significant social consequences, it is very important to detect any anomaly that can occur before an accident occurs.
In order to ensure safety, sensors have been installed in vehicles and industrial machinery at various locations to monitor operations, and the measurement data obtained from these sensors has been analyzed by computers to detect anomalies.
For example, the temperature at major locations in a vehicle can be used to detect anomalies. The temperature can be measured using a laser measuring device installed near the path of a vehicle. In this way, early anomaly detection can be performed based on the measured data.
Here, knowledge related to devices used to detect anomalies is incorporated into the computers performing the analysis.
However, anomaly detection in our knowledge base has not yet reached the point of being sufficiently reliable. At this point, the reliability of anomaly detection is being increased by using anomaly patterns detected in the past and reducing the possibility of overlooking cases similar to those in the past. The related technologies described in the following literature are known.
Laid-open Patent Publication No. 7-280603 describes the use of samples in an anomaly detecting method for machinery.
International Patent Publication No. W02008/114863 describes the calculation of the degree of similarity between patterns of change in objects observed using diagnostic equipment.
Laid-open Patent Publication No. 2008-58191 describes the calculation of the degree of similarity between standard parameter values as confidence factors in a diagnostic method for rotating machinery.
Laid-open Patent Publication No. 2009-76056 describes the use of anomaly frequency measurements in a method used to identify anomalous values.
Laid-open Patent Publication No. 2010-78467 describes a method in which a correlation coefficient matrix is created with time-series data for testing purposes and normal time-series data for reference purposes, a sparse accuracy matrix is created in which each correlation coefficient matrix is an inverse matrix, and a localized probability distribution is created for the time-series data for testing purposes and the normal time-series data for reference purposes, preferably using the accuracy matrix in a multivariate Gaussian model.
X. Zhu, Z. Ghahramani, “Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions” in Proceedings of the ICML, 2003 describes semi-supervised learning based on a Gaussian random field model, and discloses labeled data and unlabeled data represented as vertices in a weighted graph.
A. B. Goldberg, X. Zhu, and S. Wright, “Dissimilarity in Graph-Based Semi-Supervised Classification” in AISTATS, 2007 describes a semi-supervised classification algorithm in which learning occurs based on the degree of similarity and dissimilarity between labeled data and unlabeled data.